Together AI is a hosted inference platform for teams that want access to many open and open-adjacent model families through one API. Its product surface extends beyond serverless text generation to fine-tuning, dedicated endpoints, GPU clusters, image and video generation, embeddings, reranking, and code execution.
The live modules above pull current tracked token rates from our daily dataset. Use the Together AI pricing page for the full catalog and price history, or change the token mix in the AI token calculator.
Which Together AI Model Should You Use?
| Workload | Start with | Escalate to | Measure |
|---|---|---|---|
| Classification, routing, and short summaries | GPT-OSS 20B | Qwen3.5 9B | Valid output rate and latency |
| General production text with repeated context | MiniMax M3 | DeepSeek V4 Pro | Cache hit rate and accepted answers |
| Coding agents and repository work | Kimi K2.7 Code | DeepSeek V4 Pro | Cost per accepted change |
| Hard reasoning | DeepSeek V4 Pro | A proprietary frontier baseline | Correct answers after verification |
| Broad model evaluation | A small Together model basket | Dedicated endpoint for the winner | Quality, throughput, and total cost |
Cheap serverless models are useful for high-volume tasks only when they finish the job reliably. A route that needs frequent retries, longer prompts, or a second model for repair can cost more per accepted result than its token rate suggests.
Together’s catalog changes quickly. Pin model identifiers in production, keep an evaluation set, and confirm status in the official serverless model catalog before a migration.
How Together AI Pricing Works
Serverless text inference is usage-based: input, cached input where supported, and output are metered separately. The live table above is the current source for the selected models; it avoids embedding rates that can become stale in prose.
Together also sells products with different billing units:
| Product | Billing driver | Best fit |
|---|---|---|
| Serverless inference | Model usage | Testing, bursty traffic, and multi-model routing |
| Dedicated model endpoint | Reserved GPU capacity over time | Stable high-throughput production traffic |
| Fine-tuning | Training method, model, and training volume | Repeated specialist tasks with good training data |
| GPU cluster | Hardware type, quantity, and reservation term | Custom training or infrastructure-controlled inference |
| Media and utility APIs | Modality-specific usage | Images, video, audio, embeddings, reranking, and sandboxed code |
The official Together AI pricing page says users can start free and separates serverless inference, dedicated inference, GPU clusters, fine-tuning, sandbox, and storage. Treat starter access as evaluation capacity rather than a production budget; available credits and account terms can change.
Hidden Costs and Cost Controls
Output length matters. Reasoning and coding models can generate long traces, patches, or explanations. Set output caps and ask for structured results when prose adds no value.
Caching is model-specific. Some tracked Together routes expose cached-input pricing while others do not. Do not assume every model receives the same discount; verify the selected endpoint and measure actual cache usage.
Retries multiply spend. Timeouts, malformed tool calls, and failed validation can repeat both prompt and completion charges. Track attempts per accepted task, not only cost per request.
Dedicated capacity can sit idle. A dedicated endpoint may improve predictable throughput, but utilization determines whether it beats serverless. Compare hourly traffic, concurrency, latency targets, warm-up behavior, and the cost of unused capacity.
Fine-tuning has lifecycle costs. Dataset cleaning, evaluation, retraining, adapter hosting, and regression testing belong in the budget. Fine-tune only when a stable repeated task produces enough volume to repay that work.
Serverless vs Dedicated vs Fine-Tuning
Start with serverless inference when traffic is uncertain or you are comparing several model families. Together documents an OpenAI-compatible interface, so an existing OpenAI client can usually test Together endpoints with a different base URL, API key, and model name. Compatibility does not guarantee identical tool-call behavior, response fields, or output quality; keep integration tests.
Move to a dedicated endpoint when sustained utilization, predictable latency, isolation, or reserved throughput outweigh serverless flexibility. Before committing, replay a representative traffic trace and calculate utilization by hour rather than using a monthly average.
Use fine-tuning for a narrow, repeatable behavior that prompting and retrieval cannot deliver efficiently. Hold out an evaluation set and compare the tuned model against both its base model and a stronger untuned route.
Together AI vs Other Providers
Together is strongest when catalog breadth, open-model experimentation, customization, and a path from serverless testing to dedicated infrastructure matter. Groq pricing is the useful comparison when latency is the primary constraint. DeepSeek pricing shows the direct-provider alternative for DeepSeek workloads, while OpenAI pricing provides a proprietary baseline.
For a focused side-by-side decision, read Together AI vs OpenAI pricing. If you are evaluating another managed open-model host, benchmark Novita’s model catalog on the same prompts and acceptance criteria.
Affiliate disclosure: we may earn a commission if you use the Novita link above, at no extra cost to you. Compensation does not affect our pricing data or recommendations.
FAQ
Does Together AI offer free access?
Together’s official pricing page says users can start free. Check the signup flow for the current credit amount and restrictions, and use free access for evaluation rather than production capacity planning.
Is Together AI cheaper than OpenAI?
Many Together-hosted open-model routes have lower token rates than premium proprietary models, but price alone does not decide total cost. Compare retries, output length, latency, and task success in the calculator and your own evaluation set.
Does Together AI support prompt caching?
Cached-input pricing is available on selected tracked routes, not as a universal assumption for the catalog. Confirm the exact model endpoint and monitor cache hits in production.
Can I use the OpenAI SDK with Together AI?
Together documents compatibility with OpenAI Python and TypeScript clients. Change the endpoint, credential, and model identifier, then test streaming, tools, structured output, and error handling before production.
When should I use a dedicated endpoint?
Consider dedicated inference when traffic is sustained and predictable or when reserved throughput and isolation are required. Serverless is usually the safer starting point for experiments and uneven traffic.
Bottom Line
Together AI is best treated as a model platform, not a single-model bet. Start with a small serverless shortlist, route each workload to the least expensive model that passes quality checks, and measure cost per accepted task. Reserve dedicated capacity or fine-tune only after real traffic shows that the added commitment pays back.